The usage of antibiotics has been, and remains, a topic of utmost importance; on the one hand, for animal breeders, and on the other hand, for food safety. Although many countries have established strict rules for using antibiotics in animal husbandry for the food industry, their misuse and irregularities in compliance with withdrawal periods are still identified. In addition to animal-origin foods that may cause antibiotic residue problems, more and more non-animal-origin foods with this type of non-compliance are identified. In this context, we aim to summarize the available information regarding the presence of antibiotic residues in food products, obtained in various parts of the world, as well as the impact of consumption of food with antibiotic residues on consumer health. We also aim to present the methods of analysis that are currently used to determine antibiotic residues in food, as well as methods that are characterized by the speed of obtaining results or by the possibility of identifying very small amounts of residues.

Several processed food products may contain toxic compounds considered risk factors for human health. Known for its possible carcinogenic effects, acrylamide is an organic compound periodically analyzed by the entities responsible for consumer safety. Knowing the acrylamide content of food offers the possibility of implementing corrective measures when needed, targeted at lowering its level. The aim of the paper was to screen for the presence of acrylamide in four products consumed almost daily in Romania and calculate acrylamide exposure by consuming one serving. Expressed in µg/kg coffee has the highest average acrylamide content (199), followed by potato chips (134), pretzels (120), and bread (14). Results regarding the acrylamide content in one serving showed the highest levels of acrylamide in pretzels (10.20 µg/serving), followed by potato chips (4.00 µg/serving), coffee (2.20 µg/cup), and bread (0.40 µg/slice). The calculation of the acrylamide content for one serving of the studied products will facilitate the following studies on the dietary acrylamide intake of the Romanian population, studies which, to our knowledge, have not been performed so far.

The effects of genotype and diet on growth performance, carcass traits and blood metabolites were investigated. The commercial Ross 308 (R) chickens genotype, a local Black Transylvanian Naked Neck (BTNN) breed, and their crosses were used in an 81-day study. A total of 720 one-d-old chicks were allotted into eight groups in a 4 × 2 factorial design with 4 genotypes: Rmale × Rfemale (R), BTNNmale × Rfemale (BTNN-R), BTNNmale × BTNNfemale (BTNN), Rmale × BTNNfemale (R-BTNN), and 2 diets: control and low-metabolisable energy (LME). Genotype affected performance parameters, namely body weight gain (BWG), feed intake (FI), energy intake (EI), feed conversion ratio (FCR), energy conversion ratio (ECR), and production efficiency factor (PEF), irrespective of growth phase (p < 0.05). Diet had no significant effect on overall BWG, EI, ECR and PEF, except that it increased FI and FCR. Genotype influenced the carcass and organ yields (p < 0.05), except bursa weight, while diet had no significant effect. Blood parameters (total cholesterol, triglycerides, glucose, albumin and phosphorus) were affected only by genotype (p < 0.05). In summary, results show that from the two crossbreedings obtained between R and BTNN genotypes, the BTNN-R growth performance and carcass traits were superior to R-BTNN, even though both have had a similar improved plasma response. Lowering the ME level did not significantly affect the BWG but increased FI and FCR, whereas the production index was similar regardless of the genotype. Based on the present results, we concluded that the BTNN-R crosses are the most suitable for use in alternative rearing systems for slow-growing chickens.

This research introduces an innovative platform designed to manage greenhouse gas (GHG) emissions in mixed farms. Emphasizing the urgent need to address GHG emissions, particularly methane (CH₄) and nitrous oxide (N₂O), the platform targets mixed farming systems where the interplay of livestock and crop production significantly contributes to environmental impacts. Our methodology is grounded in comprehensive data collection, encompassing soil data, energy consumption, and detailed livestock information. Utilizing the Agricultural Internet of Things (AIoT), it facilitates real-time data acquisition and analysis, providing insights into various farm activities’ GHG emissions. This approach allows for precise monitoring and management of emissions from different sources, including enteric fermentation in livestock and fertilizer use in crop production. Results from the application show its effectiveness in offering a clear and interactive visualization of GHG emissions, aiding farmers in making informed decisions for sustainable farm management. The platform’s user management system, coupled with advanced data processing and visualization capabilities, underscores its potential as a vital tool for sustainable farming. Conclusively, Solution4Farming represents a significant advancement in digital agriculture, combining IoT technology with sustainable practices. Though initially designed for Romanian cattle farming, Solution4Farming’s anticipated expansion to various farming environments suggests a broader impact and relevance in sustainable agriculture.

Agricultural specialization has increased considerably in Europe over the last decades, leading to the separation of crop and livestock production at both farm and regional levels. Such a transformation is often associated with higher environmental burdens due to excessive reliance on exogenous inputs and manure management issues. Reconnecting crop and livestock production via mixed farming systems (MFSs) could improve circularity and resilience, leading to reduced environmental impacts. The objective of this study was to evaluate the life cycle environmental performance of a commercial mixed crop–dairy cattle farm in Romania and to compare it against the corresponding specialized systems. The evaluation covered both dairy cattle production (milk and meat) and cash crops. Overall, the results show that the coupled system improves environmental performance by reducing the over-reliance on high-impact inputs like synthetic fertilizers and exogenous feed. The carbon footprint for the milk production of the studied system (1.17 kg CO₂ eq.) per kg of fat- and protein-corrected milk (FPCM) was 10% lower than the mean value of common intensive milk production systems. The eutrophication impacts (2.52 × 10⁻⁴ kg P eq and 2.67 × 10⁻⁴ kg N eq./kg of FPCM) presented values of one order of magnitude less than their specialized counterparts. However, the impacts of the studied MFS, albeit lower than those for comparable specialized systems, still remain relatively high. In particular, methane emissions from enteric fermentation (0.54 kg CO₂ eq./kg FPCM) were a major contributor to the carbon footprint. This highlighted the need to address the elevated emissions from enteric fermentation with better feed management, as well as improving and reinforcing the system’s self-sufficiency.

Farming livestock—cattle, sheep, goats, pigs, and chickens—contributes to the air pollution of the atmosphere. Agricultural air pollution comes mainly in the form of ammonia, which enters the air as a gas from heavily fertilized fields and livestock waste. A reduction in air pollutants from the livestock sector can be achieved by reducing production and consumption, lowering the emission intensity of production, or combining the two. This work proposes an approach for assessing the air pollutant emissions derived from intensive cattle farming. For doing this, the animal feed, the animal behavior, and characteristics and the stable environment data are monitored and collected by a cloud platform. Specifically, Internet of Things (IoT) devices are installed in the farm and key air pollutant parameters from the stable environment (such as CO, NH₃, PM₁, PM_2.5, PM₁₀) are monitored. In this scope, a study about monitoring air pollutants is conducted, showing the most relevant platforms used in this domain. Additionally, the paper presents a comparison between the estimated and monitored air pollutants (AP), showing the fluctuation of the measured parameters. The key takeaway of the study is that ammonia concentration has a higher level during the night, being influenced by the ventilation system of the farm.